Operations | Monitoring | ITSM | DevOps | Cloud

Getting paged pulls engineers away from meaningful work, yet incident response in many organizations remains manual, reactive, and draining. An alert fires and teams scramble to find the root cause, relying on siloed knowledge, incomplete context, and a few on-call experts who are already stretched thin. The rise of AI coding agents has only intensified this challenge: As teams ship code faster with less human oversight, production systems grow increasingly complex and harder to understand.

To efficiently optimize your LLM application before pushing to production, you need a comprehensive testing and evaluation framework. By running experiments, you can optimize prompts, fine-tune temperature and other key parameters, test complex agent architectures, and understand how your application may respond to atypical, complex, or adversarial inputs. However, it can be difficult to manage your experiment runs and aggregate the results for meaningful analysis.

Developers lose hours each week to a familiar troubleshooting loop: chase down telemetry across dashboards, decipher vague errors, and juggle alerts to find the signal worth fixing. Production issues, performance regressions, and security vulnerabilities all demand attention, but they often come with little context for taking action.

Today’s IT environments span legacy infrastructure, multiple cloud platforms, and edge systems—each producing fragmented data, inconsistent signals, and hidden points of failure. This scale brings opportunity, but also operational strain: fragmented visibility, overwhelming alert noise, and slower time to resolution. With good reason, public and private sector organizations alike are moving beyond basic visibility, demanding hybrid observability that’s context-aware and action-oriented.

With the latest iteration of the GitKraken CLI, you can now connect to a local MCP server to deliver more functionality to your agent of choice. Whether you are using GitHub Copilot, Cursor, Windsurf, or any other tool, you can now leverage the power of GitKraken’s MCP server to enhance your workflows.

As development teams adopt AI-powered tools and build services that make use of AI agents, they want to extend their AI capabilities to incorporate familiar tools and observability data. However, AI agents struggle with regular API endpoints and frequently fail when parsing complex nested JSON hierarchies or incorrectly handling errors. As a result, these agents often fail to retrieve relevant results.